Mean Sea Surface Models Research Articles

The performance of Tiangong-2 InIRA in gravity anomaly recovery from fused conventional nadir-observing altimeter data

Satellite altimetry has emerged as the primary data source for deriving marine gravity, owing to advancements in satellite altimetry technologies. The Tiangong-2 interferometric imaging radar altimeter (TG2 InIRA) is a new generation wide-swath altimeter (WSA) launched by China on 15 September 2016. TG2 InIRA employs a short-baseline interferometry measurement method at a small incidence angle, thereby enabling the precise measurement of wide-swath sea surface heights (SSHs). The present study demonstrates the efficacy of utilizing data from the TG2 InIRA in conjunction with data from the CryoSat-2 (CS2) satellite to recover marine gravity anomalies. In the Northwest Pacific region (15°N ∼ 25°N, 140°E ∼ 150°E), the TG2 InIRA data were filtered to a 2 km × 2 km grid and any anomalous values were excluded using a mean sea surface model. Deflections of the vertical (DOVs) were calculated using the remove-restore technique, and the inverse Vening-Meinesz formula was applied to invert the gravity anomaly. A power spectral density analysis was conducted to assess variations in gravity anomalies at ship locations in the frequency domain. The results, based on the SIO V31.1 (DOV) model, indicate that the gridded vertical deflections derived from CS2 and TG2 InIRA data achieve more balanced accuracy in the east–west and north–south directions. The integration of TG2 InIRA and CS2 data yielded gravity results exhibiting a root mean square (RMS) of 0.519 mGal lower than those derived from TG2 InIRA data alone, and 0.034 mGal lower than those derived from CS2 data. These findings were derived from shipborne gravity data. Furthermore, the inversion of gravity anomalies from TG2 InIRA demonstrated an enhanced capability in the reduction of high-frequency gravity noise.

Altimeter Calibrations in the Preliminary Four Years’ Operation of Wanshan Calibration Site

In order to accomplish the calibration and validation (Cal/Val) of altimeters, the Wanshan calibration site (WSCS) has been used as a calibration site for satellite altimeters since its completion in August 2019. In this paper, we introduced the WSCS and the dedicated equipment including permanent GNSS reference stations (PGSs), acoustic tide gauges (ATGs), and dedicated GNSS buoys (DGB), etc. placed on Zhi’wan, Wai’ling’ding, Dan’gan, and Miao’Wan islands of the WSCS. The PGSs data of Zhi’wan and Wai’ling’ding islands were processed and analyzed using the GAMIT/GLOBK (Version 10.7) and Hector (Version 1.9) software to define the datum for Cal/Val of altimeters in WSCS. The DGB was used to transfer the datum from the PGSs to the ATGs of Zhi’wan, Wai’ling’ding, and Dan’gan islands. Separately, the tidal and mean sea surface (MSS) corrections are needed in the Cal/Val of altimeters. We evaluated the global/regional tide models of FES2014, HAMTIDE12, DTU16, NAO99jb, GOT4.10, and EOT20 using the three in situ tide gauge data of WSCS and Hong Kong tide gauge data (No. B329) derived from the Global Sea Level Observing System. The HAMTIDE12 tide model was chosen to be the most accurate one to maintain the tidal difference between the locations of the ATGs and the altimeter footprints. To establish the sea surface connections between the ATGs and the altimeter footprints, a GPS towing body and a highly accurate ship-based SSH measurement system (HASMS) were used to measure the sea surface of this area in 2018 and 2022, respectively. The global/regional mean sea surface (MSS) models of DTU 2021, EGM 2008 (mean dynamic topography minus by CLS_MDT_2018), and CLS2015 were accurately evaluated using the in situ measured data and HY-2A altimeter, and the CLS2015 MSS model was used for Cal/Val of altimeters in WSCS. The data collected by the equipment of WSCS, related auxiliary models mentioned above, and the sea level data of the hydrological station placed on Dan’gan island were used to accomplish the Cal/Val of HY-2B, HY-2C, Jason-3, and Sentinel-3A (S3A) altimeters. The bias of HY-2B (Pass No. 375) was −16.7 ± 45.2 mm, with a drift of 0.5 mm/year. The HY-2C biases were −18.9 ± 48.0 mm with drifts of 0.0 mm/year and −5.6 ± 49.3 mm with −0.3 mm/year drifts for Pass No. 170 and 185, respectively. The Jason-3 bias was −4.1 ± 78.7 mm for Pass No. 153 and −25.8 ± 85.5 mm for Pass No. 012 after it has changed its orbits since April 2022, respectively. The biases of S3A were determined to be −16.5 ± 46.3 mm with a drift of −0.6 mm/year and −9.8 ± 30.1 mm with a drift of 0.5 mm/year for Pass No. 260 and 309, respectively. The calibration results show that the WSCS can commercialize the satellite altimeter calibration. We also discussed the calibration potential for a wide swath satellite altimeter of WSCS.

Practical implications in the interpolation methods for constructing the regional mean sea surface model in the eastern Mediterranean Sea

Abstract This investigation estimates a regional Mean Sea Surface (MSS) model, named SY21MSS, over the eastern Mediterranean Sea using satellite altimetry data from nine Exact Repeat Missions (ERM) and two Geodetic Missions (GM). Two interpolation methods, Least Squares Collocation (LSC) and Ordinary Kriging (OK), were employed, and statistical metrics were applied to assess their performance within a 15 km buffer from the coast. The comparison between LSC and OK techniques in the context of regional MSS modeling primarily centers on the covariance functions used by these methods. Furthermore, generalized cross-validation results indicate that OK outperforms LSC in this region. Consequently, the study recommends adopting the Kriging-based model for calculating regional MSS models in this region due to its superior performance. The investigation further explored the disparities between estimated regional MSS models and the global model DTU18MSS, highlighting a pronounced similarity between OK-SY21MSS and DTU18MSS, as evidenced by a lesser standard deviation (SD) difference compared to LSC-SY21MSS. The practical implications of this research underscore the importance of selecting an appropriate interpolation technique based on data characteristics and study area specifics. While both LSC and OK techniques are deemed viable for MSS modeling, the study emphasizes the superior performance of OK, particularly concerning covariance functions. Additionally, the results emphasize caution when applying global models in regions with significant local variations.

Gravity anomalies determined from mean sea surface model data over the Gulf of Mexico

Gravity anomalies determined from mean sea surface model data over the Gulf of Mexico

ICESat-2 for Coastal MSS Determination—Evaluation in the Norwegian Coastal Zone

Radar satellite altimeters enable the determination of the mean sea surface to centimeter accuracy, which can be degraded in coastal areas because of the lack of valid altimetry observations due to land contamination and the altimeter footprint size. In 2018, the National Aeronautics and Space Administration launched ICESat-2, a laser altimetry mission equipped with the Advanced Topographic Laser Altimeter System, providing measurements every 0.7 m in the along-track direction. Taking into account the complexity of the Norwegian coastline, this study aims to evaluate coastal observations from ICESat-2 in order to use it to update the existing mean sea surface for Norway, NMBU18. We, therefore, determined the mean sea surface using only ICESat-2 observations and compared it with mean sea level observations from 23 permanent tide gauges along the entire coast and 21 temporary tide gauges in Norway’s largest and deepest fjord, Sognefjorden. We also included two global mean sea surface models and NMBU18 for comparison. The results have shown that ICESat-2 is indeed able to provide more valid observations in the coastal zone, which can be used to improve the mean sea surface model, especially along the coast.

SARAL/AltiKa measurements for geodetic applications: Impact of drifting and mispointing phase data

The Indo-French SARAL/AltiKa mission in Ka band has provided valuable information on global oceans to cater the needs of oceanographic community. The SARAL/AltiKa has been operating in three different orbital configurations, viz. Exact Repeat Mission (ERM, March 2013 – July 2016), Drifting Phase (DP, July 2016 – February 2019) and Mispointing Phase (MP, February 2019 – till date). We investigate the utility of the SARAL/AltiKa data for geodetic applications. Global crossover analysis of Sea Surface Height (SSH) measurements from SARAL/AltiKa suggests a stable performance throughout its life with similar range of standard deviations during ERM (5.8 cm), DP (5.9 cm) and MP (5.9 cm) configurations. Two separate Mean Sea Surface (MSS) models were retrieved by adding DP and MP data from SARAL/AltiKa to the ERM stacks of SARAL/AltiKa and Jason satellite series. The quality of MSS models were evaluated based on the crossover differences with the ERM database. It is observed that the crossover adjustments have reduced the deviations in SSH errors by ∼30% for both DP-MSS and MP-MSS models. Analysis of along-track sea surface slopes suggests that 99.9% of data in ERM and DP phases and 93.8% of data in MP phase are well within 1 μrad bound corresponding to an accuracy of ∼1 mGal in free-air gravity anomaly. Further, time-series analysis of Sea Level Anomaly (SLA) from SARAL/AltiKa showed a stable performance with no significant drifts throughout its phases. Deviations in daily SLA for SARAL/AltiKa DP (10 cm) is considerably lower than that of Jason-3 (10.2 cm) and CryoSat-2 (10.2 cm) satellites. However, daily SLA for the MP phase showed slightly larger deviation (10.4 cm), owing to the larger number of outliers, which could be easily removed using a simple median filter. The present study reveals the usefulness of MP data from SARAL/AltiKa for geodetic applications.

Assessing Models of Sea Level Rise and Mean Sea Surface with Sentinel-3B and Jason-3 Altimeter Data near Taiwan: Impacts of Data Quality and Length

Studying the local sea level rise (SLR) is vital for coastal sustainability and resilience amid climate change. Using the latest altimeter data from the Sentinel-3B (S3B) and Jason-3 (J3) missions, we investigated the regional variability and accuracy of SLR rates around Taiwan and evaluated the accuracies of three mean sea surface (MSS) models: NCTUMSS, CLS15, and DTU18. NCTUMSS is a regional model for defining the new depth datum of Taiwan. The least-squares method with outlier removal was used to compute the along-track SLRs and MSS heights from S3B and J3. Our results show that the SLR rates around Taiwan in the recent 3–6 years from S3B and J3 were 2.0–3.0 mm/year higher than the global rate of 3.3 mm/year. Positive SLR rates were observed in most waters around Taiwan, but negative rates occurred in some parts of the Taiwan Strait. Short-term SLR rates from S3B and J3 were significantly different from the long-term rates determined using altimeter data from the TOPEX/POSEIDON (TP), Jason-1, Jason-2, and J3 missions from 1992.96 to 2021.92. The comparison between the along-track MSS heights from S3B and J3 and the modeled MSS heights showed that CLS15 had the highest accuracy. The DTU18 model exhibited lower sea surface heights near Penghu in the Taiwan Strait compared with CLS15 and NCTUMSS. The NCTUMSS model incorporates tide gauge measurements and Taiwan’s hybrid geoid for a smooth transition from ocean to land. It was concluded that the SLR rates around Taiwan were not uniform, and the rates provided by the three global models, as well as the along-track S3B and J3 altimeter data, suffered from problems like limited spatial resolutions and accuracies, which originated from limited altimeter data qualities and record lengths. One must be cautious about the accuracy of an MSS model for constructing a depth datum and the accuracy of an SLR model for mitigating SLR-induced hazards. We recommend updating the MSS model around Taiwan every 7 years to ensure a 2 cm accuracy requirement, considering the average SLR rate of 3.3 mm/year around Taiwan. Short-term data reflects recent sea level rise but lacks accuracy, while the long-term sea level record may be more precise but may not capture recent rates, necessitating a comprehensive approach that considers both factors for producing accurate assessments and the planning of sea level rise impacts.

A Partial Reconstruction Method for SAR Altimeter Coastal Waveforms Based on Adaptive Threshold Judgment

Due to land contamination and human activities, the sea surface height (SSH) data retrieved from altimeter coastal waveforms have poor precision and cannot provide effective information for various tasks. The along-track high-resolution characteristic of the new synthetic aperture radar (SAR) altimeter makes the retracking methods of traditional coastal waveforms difficult to apply. This study proposes a partial reconstruction method for SAR altimeter coastal waveforms. By making adaptive threshold judgments of model matching errors and repairing the contaminated waveforms based on the nearest linear prediction, the success rate of retracking and retrieval precision of SSH are significantly improved. The data from the coastal experimental areas of the Sentinel-3B satellite altimeter are processed. The results indicate that the mean proportion of waveform quality improvement brought by partial reconstruction is 80.30%, the mean retracking success rate of reconstructed waveforms is 85.60%, and the mean increasing percentage is 30.98%. The noise levels of SSH data retrieved by different methods are calculated to evaluate the processing precision. It is shown that the 20 Hz SSH precisions of the original and reconstructed coastal waveforms are 12.75 cm and 6.32 cm, respectively, and the corresponding 1 Hz SSH precisions are 2.85 cm and 1.41 cm, respectively. The results validate that the proposed partial reconstruction method has improved the SSH precision by a factor of two, and the comparison results with mean sea surface (MSS) model data further verify this conclusion.

Evaluation of spaceborne GNSS-R based sea surface altimetry using multiple constellation signals

Comparisons between different global navigation satellite system (GNSS) signals and GNSS-reflectometry (GNSS-R) satellite systems can provide valuable suggestions for future development of the GNSS-R instrument and signal processing method. This article evaluates the ocean altimetry performance of multiple GNSS constellation signals using raw intermediate frequency data collected by Cyclone GNSS (CYGNSS) and TechDemoSat-1 (TDS-1) satellites. Data used for the evaluation include observations of GPS L1, Galileo E1, and BDS B1 band signal. The specular point position and the ground-truth bistatic delay are calculated through the HALF method. After ionospheric, tropospheric, and tide corrections, the sea surface height can be retrieved; and then the height is compared with the DTU18 mean sea surface model derived one. Based on the GNSS-R satellite-collected observations, an optimal incoherent integral duration is determined. By making use of the optimal duration, the CYGNSS-based ranging delay estimating accuracy can reach up to 2.38 m, 1.98 m, and 1.91 m for GPS, Galileo and BDS, respectively; and the TDS-1 based one can reach up to 5.46 m and 3.84 m for GPS and Galileo, respectively. The results can provide suggestion on the strategies of multi-constellation observations fusion to improve the altimetry accuracy.

SDUST2020 MSS: a global 1′ × 1′ mean sea surface model determined from multi-satellite altimetry data

Abstract. This study focuses on the determination and validation of a new global mean sea surface (MSS) model, named the Shandong University of Science and Technology 2020 (SDUST2020), with a grid size of 1′×1′. This new model was established with a 19-year moving average method and fused multi-satellite altimetry data over a 27-year period (from January 1993 to December 2019). The data of HaiYang-2A, Jason-3, and Sentinel-3A were first ingested in the SDUST2020 MSS but not in any other global MSS model, such as the CLS15 and DTU18 MSS models. Validations, including comparisons with the CLS15 and DTU18 MSS models, GPS-leveled tide gauges, and altimeter data, were performed to evaluate the quality of the SDUST2020 MSS model, all of which showed that the SDUST2020 MSS model is accurate and reliable. The SDUST2020 MSS dataset is freely available at the site (data DOI: https://doi.org/10.5281/zenodo.6555990, Yuan et al., 2022).

A new Arctic MSS model derived from combined Cryosat-2 and ICESat observations

ABSTRACT Due to the existence of seasonal or perennial sea ice cover, the determination of the Arctic sea surface is more difficult than that of mid-low latitudinal oceans. Focusing on the sea surface height in the ice-covered region, this paper constructs a new Arctic mean sea surface (MSS) model, named SUST22, by combining the measurements from ICESat and Cryosat-2 missions. The lead detection methods of ICESat and Cryosat-2 are first studied and modified to acquire sea surface measurements with better accuracy. The results have shown that the standard deviation of Cryosat-2-derived Arctic sea surface height is about 3–4 cm in 10-km resolution grids, while the value of ICESat is 5–6 cm. Then the MSS construction procedure is discussed and the SUST22 MSS model is constructed. The new model is compared with the other four Arctic MSS models. The best agreement is found between SUST22 and DTU21 with an average difference of −4.0 ± 5.2 cm. These models are also validated by ICESat-2 samples. The average difference between ICESat-2 and SUST22 is 15.8 ± 7.4 cm, which shows that the new model SUST22 presents better consistency with the ICESat-2 than any of the other models.

Spectrally Consistent Mean Dynamic Topography by Combining Mean Sea Surface and Global Geopotential Model Through a Least Squares-Based Approach

The filtering procedure is usually mandatory for modeling mean dynamic topography (MDT) when a geodetic approach based on the Mean Sea Surface (MSS) and the Global Geopotential Model (GGM) is used. This is due to the inconsistent spectral contents between MSS and GGM. However, traditional isotropic filtering algorithms (e.g., Gaussian filter) consider neither the MDT locations nor their azimuth when smoothing the signal within the filtering radius. Hence, the isotropic filtering will attenuate the MDT signal near the current and filter the current signal into the surrounding ocean, which may lead to signal contamination and distortion. In this study, we set up a least squares-based (LS) approach to model MDT signal from the altimeter-derived MSS and geoid height using spherical harmonics from GGMs, where MDT is parameterized by Lagrange Basis Functions (LBFs). The design matrix is segmentally established, considering the error information of GGM in various spectral bands. Numerical experiments in the Gulf Stream show that applications of full error variance-covariance matrix or only diagonal error variance of GGM may have marginal effects on the MDT modeling. The MDT computed from this LS-based approach using the latest releases of Gravity Field and Steady-state Ocean Circulation Explorer (GOCE) geoid models, i.e., GO_CONS_GCF_2_DIR_R6 and Gravity Observation Combination 06s model (GOCO06s), have the best agreement with the comparison data, especially near the current region. Deduced geostrophic velocities based on the MDT solutions show that the LS-based approach recovers the current signal better than the Gaussian filtering by 1.8 cm/s. Estimated error map illustrates that errors are more concentrated near the coastal region.

Optimised gravity anomaly fields from along-track multi-mission satellite altimeter over Malaysian seas

Marine gravity anomalies are crucial parameters and elements for determining coastal and ocean geoid, tectonics and crustal structures, as well as offshore studies. This study aims to derive and develop a marine gravity anomaly model over Malaysian seas from multi-mission altimetry data. Universiti Teknologi Malaysia 2020 Mean Sea Surface Model is computed based on along-track data from nine satellite missions, incorporating TOPEX, Jason-1, Jason-2, ERS-2, Geosat Follow on (GFO), Envisat-1, CryoSat-2, SARAL/AltiKa, and Sentinel-3A. The data exploited are from 1993 to 2019 (27 years). Residual gravity anomaly is computed using Gravity Software, and two-dimensional planar Fast Fourier Transformation method is applied. The evaluation, selection, blunder detection, combination, and re-gridding of the altimetry-derived gravity anomalies and Global Geopotential Model data are demonstrated. Cross-validation procedure is employed for data cleaning and quality control using the Kriging interpolation method. Then, cross-validation procedure is applied to the tapering window width 200, which adopting the GECO model denotes the optimum gravity anomaly with root mean square errors in the range of ± 4.2472 mGal to ± 6.0202 mGal. The findings suggest that the estimated marine gravity anomaly is acceptable to be implemented in the marine geoid determination and bathymetry estimation over Malaysian seas. In addition, the results of this study are valuable for geodetic and geophysical applications in marine areas.

Analysis of vertical reference levels in the brazilian coast: comparing local and global approaches

There is a historical search for the unification of different geodetic reference systems in order to better integrate geodetic investigations. In particular, coastal zones pose the challenge of connecting terrestrial and oceanic references when working with different vertical reference levels. This study aims to investigate the goodness-of-fit of ocean models to local observations, as well as to update and improve reference levels (RL) in two tide gauges (TGs) in Brazil (Imbituba and Fortaleza). Local RLs were connected to a global reference system (GRS80), which allowed absolute analyses using the mean sea surface (MSS) and lowest astronomical tide (LAT) models MSS_CNES_ CLS15, DTU_15_MSS, DTU_18_MSS and DTU_15_LAT. In the selected TGs, the MSS models showed a difference of centimeters in regards to the local mean sea level (MSL) defined by the Directorate of Hydrography and Navigation (DHN). Sea Surface Topography (SSTop) values were estimated from Global Geopotential Models and MSL data. The results indicated possible inconsistencies in the global model of LAT when compared to local observations, likely due to the difficulty of modeling coastal zones.

Regional Mean Sea Surface and Mean Dynamic Topography Models Around Malaysian Seas Developed From 27 Years of Along-Track Multi-Mission Satellite Altimetry Data

Contemporary Universiti Teknologi Malaysia 2020 Mean Sea Surface (UTM20 MSS) and Mean Dynamic Topography (UTM20 MDT) models around Malaysian seas are introduced in this study. These regional models are computed via scrutinizing along-track sea surface height (SSH) points and specific interpolation methods. A 1.5-min resolution of UTM20 MSS is established by integrating 27 years of along-track multi-mission satellite altimetry covering 1993–2019 and considering the 19-year moving average technique. The Exact Repeat Mission (ERM) collinear analysis, reduction of sea level variability of geodetic mission (GM) data, crossover adjustment, and data gridding are presented as part of the MSS computation. The UTM20 MDT is derived using a pointwise approach from the differences between UTM20 MSS and the local gravimetric geoid. UTM20 MSS and MDT reliability are validated with the latest Technical University of Denmark (DTU) and Collecte Localisation Services (CLS) models along with coastal tide gauges. The findings presented that the UTM20, CLS15, and DTU18 MSS models exhibit good agreement. Besides, UTM20 MDT is also in good agreement with CLS18 and DTU15 MDT models with an accuracy of 5.1 and 5.5 cm, respectively. The results also indicate that UTM20 MDT statistically achieves better accuracy than global models compared to tide gauges. Meanwhile, the UTM20 MSS accuracy is within 7.5 cm. These outcomes prove that UTM20 MSS and MDT models yield significant improvement compared to the previous regional models developed by UTM, denoted as MSS1 and MSS2 in this study.

Construction of the Mean Sea Surface Model Combined HY-2A With DTU18 MSS in the Antarctic Ocean

A new Mean Sea Surface (MSS) model called Shandong University of Science and Technology Antarctic Mean Sea Surface model (SDUST_ANT MSS) in the Antarctic Ocean is presented and validated in this paper. The SDUST_ANT MSS updates the DTU18 MSS with 6 years of Exact Repeat Mission (ERM) and Geodetic Mission (GM) data from HY-2A. Collinear adjustment was applied to all the ERM data to obtain the along-track mean sea surface height. Oceanic variability has been removed from the GM data. Crossover adjustment was applied to both the ERM and GM data. We constructed the HY-2A_MSS using HY-2A altimetry data based on optimal interpolation method. Several types of errors (such as white noise, residual effect of oceanic variability, and long wavelength bias) have been taken into account for the determination of MSS using optimal interpolation method. The SDUST_ANT MSS was constructed by mapping HY-2A_MSS onto the DTU18 MSS. The SDUST_ANT MSS was compared with DTU18 MSS and CNES_CLS15 MSS. At wavelengths below 150 km, differences between models are consistent with seafloor topographic gradient. At wavelengths above 150 km, differences are affected by the mesoscale activities and the altimetry errors in coastal areas. The errors of the three models, as indicated by their power spectral densities (PSDs), are of similar orders of magnitude. The absolute error is slightly smaller in SDUST_ANT than in CNES_CLS15 or DTU18.

Mean Dynamic Topography Modeling Based on Optimal Interpolation from Satellite Gravimetry and Altimetry Data

Mean dynamic topography (MDT) is crucial for research in oceanography and climatology. The optimal interpolation method (OIM) is applied to MDT modeling, where the error variance–covariance information of the observations is established. The global geopotential model (GGM) derived from GOCE (Gravity Field and Steady-State Ocean Circulation Explorer) gravity data and the mean sea surface model derived from satellite altimetry data are combined to construct MDT. Numerical experiments in the Kuroshio over Japan show that the use of recently released GOCE-derived GGM derives a better MDT compared to the previous models. The MDT solution computed based on the sixth-generation model illustrates a lower level of root mean square error (77.0 mm) compared with the ocean reanalysis data, which is 2.4 mm (5.4 mm) smaller than that derived from the fifth-generation (fourth-generation) model. This illustrates that the accumulation of GOCE data and updated data preprocessing methods can be beneficial for MDT recovery. Moreover, the results show that the OIM outperforms the Gaussian filtering approach, where the geostrophic velocity derived from the OIM method has a smaller misfit against the buoy data, by a magnitude of 10 mm/s (17 mm/s) when the zonal (meridional) component is validated. This is mainly due to the error information of input data being used in the optimal interpolation method, which may obtain more reasonable weights of observations than the Gaussian filtering method.

Quantifying the precision of retracked Jason-2 sea level data in the 0–5 km Australian coastal zone

This paper aims to quantify the precision of retracked Jason-2 altimeter sea level data (2008–2016) in the 0–5 km Australian coastal zone. A new retracking strategy has been developed to achieve the highest possible precision of coastal sea-level data. Coastal waveforms were classified into four groups according to the adaptability of dedicated coastal retrackers, from which each group was reprocessed using an optimal retracker. A modified Brown model was introduced to reprocess the quasi-specular waveforms over calm waters. A seamless transition of sea surface heights from different retrackers was achieved by estimating and removing the significant wave height dependent height differences. In order to generate the 20-Hz sea level anomaly (SLA) and total water level envelope (TWLE) at the coast, the regional along-track mean sea surface (MSS) with a high spatial resolution (~300 m) was estimated. Compared to the global MSS model, the use of the along-track MSS has dramatically reduced the SLA variance by 140 cm2 in the 0–15 km coastal strip.The results from the evaluation of data precision for Jason-2 ground tracks and the validation against tide gauges show that this new retracking strategy can retrieve more reliable sea level data nearshore. The precision of 20-Hz sea level data in the 0–5 km Australian coastal strip ranges typically from 5 cm to 6 cm in most coastal areas, which is only ~1 cm lower than that beyond 5 km off the coastline. However, in Northwest Australian and some coastal regions, the data precision drops to ≥7 cm due partly to the specific coastal sea states and topography. In addition, it is found that the altimeter and tide gauge TWLE time series have a high correlation (>0.8) in the 0–5 km distance band, indicating that coastal sea-level data are useful for applications such as estimation of river plumes, long-term variation of water level in lakes and rivers, and the analysis of storm surges.

Arctic Sea Ice Freeboard Retrieval from Envisat Altimetry Data

Arctic sea ice variations are sensitive to Arctic environmental changes and global changes. Freeboard and thickness are two important parameters in sea ice change research. Satellite altimetry can provide long-time and large-scale sea ice monitoring. We estimated the Arctic sea ice freeboard and its variations for the period from 2002 to 2012 from Envisat satellite altimetry data. To remove geoid undulations, we reprocessed the Envisat data using a newly developed mean sea surface (MSS) model, named DTU18. Residuals in the static geoid were removed by using the moving average technique. We then determined the local sea surface height and sea ice freeboard from the Envisat elevation profiles. We validated our freeboard estimates using two radar freeboard products from the European Space Agency (ESA) Climate Change Initiative (CCI) and the Alfred Wegener Institute (AWI), as well as the Operation IceBridge (OIB) sea ice freeboard product. The overall differences between our estimates and the CCI and AWI data were 0.11 ± 0.14 m and 0.12 ± 0.14 m, respectively. Our estimates show good agreement with the three products for areas of freeboard larger than 0.2 m and smaller than 0.3 m. For areas of freeboard larger than 0.3 m, our estimates correlate better with OIB freeboard than with CCI and AWI. The variations in the Arctic sea ice thickness are discussed. The ice freeboard reached its minimum in 2008 during the research period. Sharp decreases were found in the winters of 2005 and 2007.

Sea Surface Heights and Marine Gravity Determined from SARAL/AltiKa Ka-band Altimeter Over South China Sea

SARAL/AltiKA (SRL) is the first altimetry satellite with a Ka-band altimeter. To validate the advantages of the Ka-band altimeter over traditional Ku-band altimeters in marine geodetic applications, a comprehensive analysis is carried out over the South China Sea (SCS) (0–30° N, 105–125° E) from three aspects, namely the influence of load on waveforms, the precision of sea surface heights (SSHs), and the precision of altimeter-derived marine gravity field. Coastal waveforms of SRL, Jason-2, and CryoSat-2 are separately compared with corresponding ocean-type waveforms. The radius of coastal influence on SSHs of SRL/exact repeat mission (SRL/ERM) is the smallest, being about 3 km. Crossover discrepancies, global mean sea surface models, and tide gauge data are used to assess the precision of altimetric SSHs. Compared with the SSH precision of Ku-band Jason-2/ERM, the SSH precision of Ka-band SRL/ERM is 4.6% higher over the SCS and 10% higher in offshore areas. Gridded gravity anomalies are derived from measurements of SRL/drifting phase (SRL/DP) and CryoSat-2 through the inverse Vening-Meinesz formula, respectively. According to the assessment by shipborne gravity data and global marine gravity models, the precision of SRL/DP-derived gravity is higher than that of CryoSat-2-derived gravity over the SCS, especially in offshore areas. In some cycles, ground tracks of SRL/ERM have large drifting of more than 10 km from nominal tracks. The results show that the Ka-band altimeter of SRL has better precision in SSHs and marine gravity recovery than the Ku-band altimeter over the SCS, particularly in offshore areas.